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Full-Text Articles in Signal Processing
Towards The Mitigation Of Correlation Effects In The Analysis Of Hyperspectral Imagery With Extension To Robust Parameter Design, Jason P. Williams
Towards The Mitigation Of Correlation Effects In The Analysis Of Hyperspectral Imagery With Extension To Robust Parameter Design, Jason P. Williams
Theses and Dissertations
Standard anomaly detectors and classifiers assume data to be uncorrelated and homogeneous, which is not inherent in Hyperspectral Imagery (HSI). To address the detection difficulty, a new method termed Iterative Linear RX (ILRX) uses a line of pixels which shows an advantage over RX, in that it mitigates some of the effects of correlation due to spatial proximity; while the iterative adaptation from Iterative Linear RX (IRX) simultaneously eliminates outliers. In this research, the application of classification algorithms using anomaly detectors to remove potential anomalies from mean vector and covariance matrix estimates and addressing non-homogeneity through cluster analysis, both of …
Multi-Dimensional Classification Algorithm For Automatic Modulation Recognition, Ouail Albairat
Multi-Dimensional Classification Algorithm For Automatic Modulation Recognition, Ouail Albairat
Theses and Dissertations
This thesis proposes an approach for modulation classification using existing features in a more efficient way. The Multi-Dimensional Classification Algorithm (MDCA) treats features extracted from signals of interest as elements with irrelevant identities, hence eliminating any dependence of the classifier on any particular feature. This design enables the use of any number of features, and the MDCA algorithm provides the capability to classify modulations in higher dimensions. The use of multiple features requires an equal number of data dimensions, and thus classification in as high a dimensional space as possible can improve final classification results. Finally, the MDCA algorithm uses …
Automatic Target Recognition User Interface Tool, David A. Kerns
Automatic Target Recognition User Interface Tool, David A. Kerns
Theses and Dissertations
A computer tool to aid in selecting the best Automatic Target Recognition (ATR) algorithm is developed. The program considers many quantifiers, accepts user-defined parameters, allows for changes in the operational environment and presents results in a meaningful way. It is written for Microsoft Excel. An ATR algorithm assigns a class label to a recognized target. General designations can include "Friend" and "Foe." The error of designating "Friend" as "Foe" as well as "Foe" as "Friend" comes with a high cost. Studying each algorithm's error can minimize this cost. Receiver Operating Characteristic (ROC) curves provide only information on the probabilities given …
Daytime Detection Of Space Objects, Alistair D. Funge
Daytime Detection Of Space Objects, Alistair D. Funge
Theses and Dissertations
Space Situational Awareness (SSA) requires repeated object updates for orbit accuracy. Detection of unknown objects is critical. A daytime model was developed that evaluated sun flares and assessed thermal emissions from space objects. Iridium satellites generate predictable sun glints. These were used as a model baseline for daytime detections. Flares and space object thermal emissions were examined for daytime detection. A variety of geometric, material and atmospheric characteristics affected this daytime detection capability. In a photon noise limited mode, simulated Iridium flares were detected. The peak Signal-to- Noise Ratios (SNR) were 6.05e18, 9.63e5, and 1.65e7 for the nighttime, daytime and …
Classification Of Radar Targets Using Invariant Features, Gregory J. Meyer
Classification Of Radar Targets Using Invariant Features, Gregory J. Meyer
Theses and Dissertations
Automatic target recognition ATR using radar commonly relies on modeling a target as a collection of point scattering centers, Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the …
Space Object Identification Using Feature Space Trajectory Neural Networks, Neal W. Bruegger
Space Object Identification Using Feature Space Trajectory Neural Networks, Neal W. Bruegger
Theses and Dissertations
The Feature Space Trajectory Neural Network (FSTNN) is a simple yet powerful pattern recognition tool developed by Neiberg and Casasent for use in an Automatic Target Recognition System. Since the FSTNN was developed, it has been used on various problems including speaker identification and space object identification. However, in these types of problems, the test set represents time series data rather than an independent set of points. Since the distance metric of the standard FSTNN treats each test point independently without regard to its position in the sequence, the FSTNN can yield less than optimal results in these problems. Two …
Space Object Identification Using Spatio-Temporal Pattern Recognition, Gary W. Brandstrom
Space Object Identification Using Spatio-Temporal Pattern Recognition, Gary W. Brandstrom
Theses and Dissertations
This thesis is part of a research effort to automate the task of characterizing space objects or satellites based on a sequence of images. The goal is to detect space object anomalies. Two algorithms are considered - the feature space trajectory neural network (FST NN) and hidden Markov model (HMM) classifier. The FST NN was first presented by Leonard Neiberg and David P. Casasent in 1994 as a target identification tool. Kenneth H. Fielding and Dennis W. Ruck recently applied the hidden Markov model classifier to a 3D moving light display identification problem and a target recognition problem, using time …
Spatio-Temporal Pattern Recognition Using Hidden Markov Models, Kenneth H. Fielding
Spatio-Temporal Pattern Recognition Using Hidden Markov Models, Kenneth H. Fielding
Theses and Dissertations
A new spatio-temporal method for identifying 3D objects found in 2D image sequences is presented. The Hidden Markov Model technique is used as a spatio-temporal classification algorithm to identify 3D objects by the temporal changes in observed shape features. A new information theoretic argument is developed that proves identifying objects based on image sequences can lead to higher classification accuracies than single look methods. A new distance measure is proposed that analyzes the performance of Hidden Markov Models in a multi-class pattern recognition problem. A three class problem identifying moving light display objects provides experimental verification of the sequence processing …